![]() ![]() ![]() Given the widespread adoption of "validation" as the colloquial termįor this process, we will consistently use it in our documentation. Precisely conforms to the applied type hints. In essence, Pydantic's primary goal is to assure that the resulting structure post-processing (termed "validation") Refer to the Data Conversion and Attribute Copies sections below. For a more in-depth understanding of the implications for your usage, Without mutating the original input data. This can involve copying arguments passed to the constructor in order to perform coercion to a new type In some cases, "validation" goes beyond just model creation, and can include the copying and coercion of data. While this distinction may initially seem subtle, it holds practical significance. When data cannot be successfully parsed into a model instance. This distinction becomes apparent when considering that Pydantic's ValidationError is raised Pydantic guarantees the types and constraints of the output, not the input data. In Pydantic, the term "validation" refers to the process of instantiating a model (or other type) that adheres to specified The action of checking or proving the validity or accuracy of something. Primary focus doesn't align precisely with the dictionary definition of "validation": validation ¶ The potential confusion around the term "validation" arises from the fact that, strictly speaking, Pydantic's This task, which Pydantic is well known for, is most widely recognized as "validation" in colloquial terms,Įven though in other contexts the term "validation" may be more restrictive. We use the term "validation" to refer to the process of instantiating a model (or other type) that adheres to specified types andĬonstraints. Validation - a deliberate misnomer TL DR ¶ ![]()
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